Vehicle identification driven by augmented reality (ar)

ABSTRACT

A device receives user rendering data for a 3-D rendering of a user. The 3-D rendering is a proportional representation of the user and the user rendering data is available via an application. The device determines user characteristics of the user. The device receives an indication that a user device has submitted a vehicle search request. The device identifies vehicles to recommend to the user based on an analysis of: the user characteristics, and vehicle characteristics for a collection of vehicles being offered via the application. The device causes vehicle description data for the vehicles to be displayed via an interface of the application. The device receives user interaction data that indicates a user selection of vehicle. The device causes, based on receiving the user interaction data, the interface of the application to display a placement of the 3-D rendering of the user into a 3-D rendering of the vehicle.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/946,549, filed Jun. 26, 2020 (now U.S. Pat. No. 11,636,526), which isa continuation of U.S. patent application Ser. No. 16/547,368, filedAug. 21, 2019 (now U.S. Pat. No. 10,699,323), the contents of which areincorporated herein by reference in their entireties.

BACKGROUND

Augmented reality (AR) may refer to a live view of a physical,real-world environment that is modified by a computing device to enhancean individual's current perception of reality. In augmented reality,elements of the real-world environment are “augmented” bycomputer-generated or extracted input, such as sound, video, graphics,haptics, global positioning system (GPS) data, and/or the like.Augmented reality may be used to enhance and/or enrich the individual'sexperience with the real-world environment.

SUMMARY

According to some implementations, a method may include receiving userrendering data for a three-dimensional (3-D) rendering of a user,wherein the 3-D rendering is a proportional representation of the user,and wherein the user rendering data is to be made available to anapplication that assists with vehicle purchases. The method may includedetermining, by processing the user rendering data, a set of usercharacteristics of the user. The method may include receiving anindication that a user device has submitted a vehicle search request.The method may include identifying a set of vehicles to recommend to theuser based on an analysis of: the set of user characteristics, and a setof vehicle characteristics for a collection of vehicles being offeredvia the application. The method may include causing vehicle descriptiondata for the set of vehicles to be displayed via an interface of theapplication. The method may include receiving user interaction data thatindicates a user selection of a particular vehicle of the set ofvehicles. The method may include causing, based on receiving the userinteraction data, the interface of the application to display aplacement of the 3-D rendering of the user into a 3-D rendering of theparticular vehicle, wherein the placement creates a visual thatillustrates a degree to which the user is compatible with the particularvehicle.

According to some implementations, a device may include one or morememories, and one or more processors, operatively coupled to the one ormore memories, configured to receive image data that depicts at least aportion of a user of an application that assists with vehicle purchases.The one or more processors may generate user rendering data for athree-dimensional (3-D) rendering of at least the portion of the userdepicted in the image data, wherein the 3-D rendering is based onmeasurements of the user. The one or more processors may determine, byprocessing the user rendering data, a set of user characteristics of theuser. The one or more processors may receive an indication that a userdevice associated with the user is accessing a search feature of theapplication. The one or more processors may identify a set of vehiclesto recommend to the user based on a machine-learning-driven analysis ofat least one of: one or more search parameters that the user deviceprovided to the search feature of the application, the set of usercharacteristics, or a set of vehicle characteristics for a collection ofvehicles being offered via the application. The one or more processorsmay cause vehicle description data for the set of vehicles to bedisplayed via an interface of the application. The one or moreprocessors may receive user interaction data that indicates a userselection of a particular vehicle of the set of vehicles. The one ormore processors may cause, based on receiving the user interaction data,the interface of the application to display a placement of the 3-Drendering of the user into a 3-D rendering of the particular vehicle,wherein the placement creates a visual that illustrates a degree towhich the user is compatible with the particular vehicle.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions. The one or more instructions,when executed by one or more processors of a device, may cause the oneor more processors to receive user rendering data for athree-dimensional (3-D) rendering of a user, wherein the 3-D renderingis based on measurements of the user and is a proportionalrepresentation of the user, and wherein the user rendering data is to bemade available to an application that assists with vehicle purchases.The one or more instructions may cause the one or more processor todetermine, by processing the user rendering data, a set of usercharacteristics of the user. The one or more instructions may cause theone or more processor to integrate the set of user characteristics intoa search feature of the application to permit the search feature tooffer a set of search parameters that are customized to the user. Theone or more instructions may cause the one or more processor to receivean indication that a user device associated with the user is accessingthe search feature of the application. The one or more instructions maycause the one or more processor to identify a set of vehicles torecommend to the user, based on an analysis of: one or more searchparameters that the user device provided to the search feature of theapplication, the set of user characteristics, and a set of vehiclecharacteristics for a collection of vehicles being offered via theapplication. The one or more instructions may cause the one or moreprocessor to provide vehicle description data for the set of vehiclesfor display via an interface of the application. The one or moreinstructions may cause the one or more processor to cause the interfaceof the application to display a placement of the 3-D rendering of theuser into a 3-D rendering of at least one vehicle of the set ofvehicles, wherein the placement creates a visual that illustrates adegree to which the user is compatible with the at least one vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2 .

FIGS. 4-6 are flowcharts of one or more example processes for usingaugmented reality (AR) to assist a user in searching for a vehicle.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Augmented reality (AR) may be used to superimpose three-dimensional(3-D) representations of objects on a display of an image of anenvironment that is being captured (e.g., in real time). For example, auser may use a camera of a user device (e.g., a smartphone, a tablet,smart glasses, and/or the like) to capture video of the user'ssurroundings, and the user device (e.g., an AR application running onthe user device) may superimpose a 3-D representation of an object onthe image being captured by the user device.

In some cases, AR may be used to assist users in completing transactionsfor products and/or services (e.g., in purchasing and/or renting theproducts and/or services). For example, a user may use the Internet toengage in a transaction for a product. In some cases, the user mayhesitate to complete the transaction because the user may want toperform an in-person inspection of the product. For example, if the useris searching for a vehicle, the user may need to sit inside one or morevehicles to make a determination as to which vehicle is a good fit forthe user. If the user is searching for the vehicle via the Internet, theuser may be deterred from completing the transaction for the vehicle.Furthermore, without a way for a vehicle search feature to identifyoptimal vehicles for the user, resources (e.g., processing resources,network resources, memory resources, and/or the like) of a user deviceand/or a server supporting the vehicle search feature may be wasted byproducing sub-optimal search results that will not cause the user tocomplete the transaction for the vehicle.

Some implementations described herein provide a vehicle search platformto assist a user in searching for a vehicle by supporting a searchfeature of a vehicle search application with AR technology. For example,the vehicle search platform may use a three-dimensional (3-D) renderingof the user to identify a set of vehicles to recommend to a user and,after the user selects a vehicle, may cause an interface of the vehiclesearch application to display the 3-D rendering of the user within the3-D rendering of the selected vehicle (e.g., by showing the 3-Drendering of the user sitting in a seat of the 3-D rendering of thevehicle). This may illustrate to the user a degree to which the user iscompatible with the selected vehicle.

In this way, the vehicle search platform uses AR to identify optimalvehicles to recommend to the user and provides the user with anillustration that shows a degree to which the user is compatible withthe selected vehicle. By identifying the optimal vehicles to recommendto the user, the vehicle search platform conserves resources that wouldotherwise be wasted processing vehicle data for other vehicles that arenot a good fit for the user. Additionally, by providing the user withthe illustration, the vehicle search platform allows the user tovisualize the degree to which the user is compatible with the vehicle,which allows the user to make more informed purchasing decisions,thereby improving customer satisfaction, conserving resources of devicesthat would otherwise be used to return a purchased vehicle (e.g., due tothe user not realizing that the vehicle does not fit properly untilafter the vehicle was purchased), and/or the like.

FIGS. 1A-1F are diagrams of one or more example implementations 100described herein. For example, implementation(s) 100 may include a datastorage device, a user device, and/or a vehicle search platform.

As shown in FIGS. 1A-1F, the vehicle search platform may use ARtechnology to support a vehicle search application in a manner that isable to assist a user in identifying a vehicle (e.g., that is to bepurchased, rented, and/or the like). Additionally, while one or moreimplementations described herein are performed by the vehicle searchplatform, it is to be understood that this is provided by way ofexample. In practice, one or more of the implementations may beperformed by the user device or by another device.

As shown in FIG. 1A, and by reference number 105, the vehicle searchplatform may integrate 3-D renderings of vehicles into a search featureof an application that assists with vehicle purchases (referred tohereafter as a vehicle search application). For example, the vehiclesearch platform may integrate 3-D renderings of vehicles, that are partof a collection of vehicles, into a search feature of the vehicle searchapplication, such that a user accessing the search feature may view the3-D renderings via an interface.

In some implementations, the vehicle search platform may host thevehicle search application. In some implementations, the vehicle searchplatform may support the vehicle search application. For example, thevehicle search application may be hosted by another device (e.g., aserver external to the vehicle search platform). In this case, one ormore functions and/or features provided by the vehicle search platformmay be provided to the vehicle search application via a plugin and oneor more application programming interfaces (APIs) or other types ofcommunication interfaces.

In some implementations, the vehicle search application may include aset of search features that may assist users in searching for vehicles.The set of search features may include a search feature that allowsusers to input search parameters while searching for particularvehicles, a search feature that allows users to recommend specific userpreferences, a search feature that allows users to search based onfeedback information, and/or the like. In this case, a data structuremay store vehicle data for the collection of vehicles that aresearchable via one or more of the set of search features, such that thevehicle data may be displayed via an interface of the vehicle searchapplication. The vehicle data for a vehicle may include a vehicleidentifier for the vehicle, vehicle characteristics data that describesa set of vehicle characteristics of the vehicle, vehicle price data thatindicates a price that is being offered for the vehicle, and/or thelike.

In some implementations, the vehicle search platform may receive vehiclerendering data that depicts the 3-D renderings of the vehicles in thecollection of vehicles. For example, an entity (e.g., an organization,an individual, and/or the like) that is selling vehicles may havealready created 3-D renderings for the vehicles. In this case, thevehicle search platform may provide receive the vehicle rendering datafor the vehicles. For example, the vehicle search platform may provide arequest to a data storage device that stores the vehicle rendering data,which may cause the data storage device to provide the vehicle searchplatform with the vehicle rendering data. As another example, anagreement may have been reached between the entity and an organizationassociated with the vehicle search platform, which may cause the datastorage device to provide the vehicle rendering data to the vehiclesearch platform.

Additionally, or alternatively, the entity that is using the vehiclesearch application to market vehicles may not have previously created3-D renderings of vehicles. In this case, an agent of the organizationassociated with the vehicle search platform may visit the dealership andmay use a 3-D camera of a user device to take images of the insideand/or the outside of the vehicles. In some cases, the user device maybe configured with 3-D rendering generation technology and may generateand provide the vehicle search platform with the vehicle rendering datathat depicts the 3-D renderings of the vehicles. In other cases, theuser device may provide the vehicle search platform with a set of imagesof the vehicles and the vehicle search platform may process the set ofimages to generate the vehicle rendering data.

In some implementations, the vehicle search platform may generatevehicle rendering data based on one or more configurable components of avehicle. For example, the vehicle search platform may receive vehiclecharacteristics data that indicates a maximum range at which one or moreseats within the vehicle may be adjusted (e.g., a seat may have a firstseat placement associated with a minimum seat extension distance and asecond seat placement associated with a maximum seat extensiondistance). In this case, the vehicle search platform may generate 3-Drenderings of the vehicle that depict the one or more seats at variousseat placements, such that a user may be able to view particular 3-Drenderings of the vehicle that depict the one or more seats at variousplacements. It is to be understood that this is provided by example, andthat in practice, the vehicle search platform may generate 3-Drenderings of vehicles for other types of adjustable components ofvehicles.

In some implementations, the 3-D renderings of the collection ofvehicles may map to a 3-D coordinate space. For example, a 3-D renderingof a vehicle may include a set of 3-D points, whereby each point has anX coordinate, a Y coordinate, and a Z coordinate. In someimplementations, the set of vehicle characteristics for the collectionof vehicles may correspond to particular points in the 3-D coordinatespace. For example, if a vehicle feature is a sunroof, a vehicle featureidentifier for the sunroof may be stored in association with a set ofpoints that define an area of the sunroof.

In some implementations, the vehicle search platform may integrate a setof 3-D renderings of the vehicles into the search feature of the vehiclesearch application. For example, the vehicle search application mayinclude an interface that displays a list of vehicles that are for sale.In this case, the vehicle search platform may integrate the 3-Drenderings of the collection of the vehicles into the application suchthat the users may be able to view the 3-D renderings via the interface.In some implementations, the 3-D renderings may be available to theusers as a plugin of the vehicle search application.

In this way, the vehicle search platform integrates the 3-D renderingsof the collection of vehicles into the vehicle search application.

As shown in FIG. 1B, and by reference number 110, a user may use theuser device to generate user rendering data that depicts a 3-D renderingof a user. The 3-D rendering of the user may be placed inside of 3-Drenderings of vehicles that the user is considering purchasing.

In some implementations, the user may use the user device to access amodel generation tool that may be used to generate the 3-D rendering ofthe user. The model generation tool may be an application hosted onand/or supported by the user device. In some implementations, the usermay log into a profile (e.g., by inputting credential information) toaccess functionality of the model generation tool.

In some implementations, the model generation tool may be part of thevehicle search application. In some implementations, the modelgeneration tool may be separate from the vehicle search application andmay be used in conjunction with the vehicle search application. Forexample, the model generation tool may be used to generate the 3-Drendering of the user and may provide the user rendering data thatdepicts the 3-D rendering of the user to the vehicle search platform. Inthis case, the vehicle search platform may integrate the user renderingdata into the vehicle search application, may use the user renderingdata to support the vehicle search application, and/or the like.

In some implementations, to ensure that the user device captures imagesthat will be sufficient for generating an accurate 3-D rendering of theuser, the model generation tool may display a set of instructions thatexplain how to capture the images. For example, the application maydisplay a set of instructions that include an instruction recommendingthat another individual capture the images of the user (e.g., to improvethe quality of the images that are captured), an instruction indicatingwhich angles to use when capturing the images, an instruction indicatinghow much of the user has to be captured by the images, an instructionindicating to the user to include a reference point in the images (e.g.,a ground, a wall, a vehicle, and/or the like), an instruction indicatinga recommended number of images to capture, and/or the like.

Before generating the user rendering data that depicts the 3-D renderingof user, as shown by reference number 110-1, the user device may firstdefine a 3-D bounding box around an image of the user. For example, theindividual who is assisting the user in creating the 3-D rendering(referred to herein as a second user) may take the user device (e.g.,which has launched the model generation tool) and may position a cameraof the user device in a manner that faces the user. This may cause theuser device (e.g., using the model generation tool) to project a 3-Dbounding box around the user (which, in some cases, may be visible via adisplay on the user device). This may allow the second user to adjustone or more boundaries of the 3-D bounding box until the 3-D boundingbox includes just the user (e.g., and not one or more other objects thatmay be in the real-world environment around the user).

As shown by reference number 110-2, the user device may capture a set ofimages of the user. For example, the second user may select a scanbutton on an interface of the model generation tool which may cause theuser device to capture one or more images of the user. In someimplementations, the second user may begin walking around the user andusing the user device to capture images of the user from differentpositions, different angles, and/or the like. In some implementations,the model generation tool may record a video of the user (e.g., insteadof capturing images of the user).

As shown by reference number 110-3, the user device may adjust an originpoint of the 3-D rendering of the user. For example, the user device mayprocess an image of the user and may identify a point of origin based onone or more configured rules (e.g., a configured rule may be used toidentify a particular part of the user, such as a hand, a foot, an eye,and/or the like). The origin point may map to a particular 3-D point ina 3-D coordinate space (e.g., a particular X coordinate, Y coordinate,and Y coordinate). In some implementations, the user device may, as partan AR scene that has been generated, present the 3-D rendering of theuser on the interface of the user device. This may allow the second userto modify the point of origin by adjusting particular coordinatesassociated with the origin point.

In some implementations, the user device may generate the 3-D renderingof the user based on the set of images. For example, the second user mayinteract with the interface of the model generation tool to indicatethat an image capturing phase of the process has been completed. Thismay cause the user device to generate the 3-D rendering of the userbased on the set of images. In this case, the 3-D rendering of the usermay map to a 3-D coordinate space (e.g., such that a 3-D point on the3-D rendering has an X coordinate, a Y coordinate, and a Z coordinate).In some implementations, the user device may automatically detect that asufficient quantity of images have been captured and may generate theuser rendering data for the 3-D rendering of the user. In someimplementations, the user device may cause an interface of the modelgeneration tool to update in order to notify the second user that asufficient quantity of images have been captured.

In some implementations, the user device may generate one or more 3-Drenderings of the one or more other users (e.g., the second user, afamily member of the user, and/or the like). Additionally, oralternatively, the user device may generate one or more 3-D renderingsof one or more in-vehicle objects. For example, the user device maygenerate a 3-D rendering of a car seat for a child, may generate a 3-Drendering of a glass or cup (e.g., which may be placed into a cup holderof a vehicle), may generate a 3-D rendering of a suitcase (e.g., whichmay be placed into a trunk of a vehicle), and/or the like. In this way,3-D renderings may be generated for the user, for one or more otherusers, and/or for one or more in-vehicle objects that may be placedwithin 3-D renderings of vehicles to assist the user in making vehiclepurchasing decisions, as described further herein.

While one or more implementations described herein describe the userdevice as generating one or more 3-D renderings, it is to be understoodthat this is provided by way of example. In other implementations, forexample, the user device may capture the set of images of the user, mayprovide the set of images to the vehicle search platform, and thevehicle search platform may generate the 3-D rendering of the user.

In this way, the vehicle search platform generates one or more 3-Drenderings (e.g., of the user, of the one or more other others, of theone or more in-vehicle objects, and/or the like) that may assist theuser in making vehicle purchasing decisions, as further described below.

As shown in FIG. 1C, and by reference number 115, the user renderingdata that depicts the 3-D rendering of the user may be provided to thevehicle search platform. For example, the user may interact with theinterface of the model generation tool and/or an interface of thevehicle search application to provide the user rendering data to thevehicle search platform. In this case, the user device may use anapplication programming interface (API) or another type of communicationinterface to provide the user rendering data to the vehicle searchplatform.

Additionally, or alternatively, particular user rendering data thatdepicts the one or more 3-D renderings of the one or more other usersmay be provided to the vehicle search platform. Additionally, oralternatively, in-vehicle object data that depicts the one or more 3-Drenderings of the one or more in-vehicle objects may be provided to thevehicle search platform.

As shown by reference number 120, the vehicle search platform maydetermine a set of user characteristics of the user. For example, thevehicle search platform may process the user rendering data using a setof image processing techniques to determine a set of usercharacteristics of the user.

The set of image processing techniques may include one or more computervision techniques, one or more object recognition techniques, one ormore characteristic recognition techniques, a technique using a gaitanalysis, one or more machine learning techniques, and/or the like. Theset of user characteristics may include a height of the user, a width ofone or more body parts of the user (e.g., a leg of the user, a shin ofthe user, a knee of the user, and/or the like), a length of the one ormore body parts of the user (e.g., the leg of the user, the shin of theuser, the knee of the user, and/or the like), a measurement of theuser's waist, a measurement of the user's shoulder and/or hip portions,an estimated weight of the user, and/or any other characteristics thatmay be used to define dimensions of the user (e.g., which may impact howthe user fits within particular vehicles).

As an example, the vehicle search platform may determine a height of theuser. For example, the user rendering data for the 3-D rendering of theuser may include a set of 3-D points that are part of a 3-D coordinatespace. In this example, the vehicle search platform may identify aheight of the user by performing a height recognition technique toidentify a first 3-D point on the user that makes contact with a groundplane (e.g., which represents the ground in the real-world environmentof the user), where the first 3-D point includes a Y coordinate that isequal to or lower than any other Y coordinate found within other 3-Dpoints on the user. Next, the vehicle search platform may identify asecond 3-D point on the user that includes an X coordinate that matchesan X coordinate included in the first 3-D point and includes a Ycoordinate that is equal to or greater than any other Y coordinate foundwithin other 3-D points on the user. This may allow the vehicle searchplatform to determine the difference between the Y coordinate in thefirst 3-D point and the Y coordinate in the second 3-D point and todetermine the height of the user based on the difference (e.g., adistance in the 3-D coordinate space may map to a distance in thereal-world environment of the user).

As shown by reference number 125, the user may interact with the userdevice to input a set of vehicle preferences. For example, the user maylog into the vehicle search application and may input a set of vehiclepreferences that indicate a preferred type of vehicle, a preferred brandof vehicle, a preferred cost or cost range when purchasing a vehicle, apreferred set of vehicle features (e.g., a type of transaction, a typeof trim, a color, and/or the like). This may cause the vehiclepreference data that identifies the set of vehicle preferences to beprovided to the vehicle search platform.

As shown by reference number 130, the vehicle search platform maygenerate profile information for a user profile that is used to accessthe vehicle search application. For example, the user may haveregistered for access to the vehicle search application by creating auser profile. In this case, the vehicle search application may generate,for the user profile, profile information that includes usercharacteristics data identifying the set of user characteristics of theuser, the vehicle preference data identifying the set of vehiclepreferences of the user, and/or the like.

In some implementations, the vehicle search platform may use the profileinformation to customize the search feature of the vehicle searchapplication for the user. For example, the vehicle search feature mayinclude a set of search parameters that allow users to filter through acollection of vehicles (e.g., which may be listed for sale).

The set of search parameters may include a parameter for searching byvehicle type, a parameter for searching by a price range, a parameterfor searching by vehicle color, a parameter for searching by mileage,and/or a set of custom search parameters, such as one or more customsearch parameters relating to a size of the user (e.g., a searchparameter that may be used to filter vehicles based on a height of theuser, a search parameter that may be used to filter vehicles based on awidth or estimated weight of the user, and/or the like), one or moresearch parameters relating to vehicle preferences (e.g., a searchparameter that may be used to filter vehicles based on a vehiclepreference of the user), and/or the like. In this case, the vehiclesearch platform may be configured with a set of search rules thatspecify how to filter the collection of vehicles based on particularsearch parameters.

As a specific example, if the user, while using the search feature ofthe vehicle search application, selects height as a search parameter,the vehicle search platform may obtain the height of the user (e.g., byreferencing the profile information) and may filter the collection ofvehicles based on a search rule indicating that certain types ofvehicles are to be excluded from search results if the height of theuser is above a threshold height (e.g., if an individual is taller than6 feet, 4 inches (6′ 4″), certain types of vehicles that are too smallfor the individual may be omitted from the search results). Additionalinformation regarding the search filter is provided further herein.

Additionally, or alternatively, the vehicle search platform may generateadditional user characteristics data for the one or more other users(e.g., in a manner described above) and/or may update the user profileand/or other user profiles based on the additional user characteristicsdata. Additionally, or alternatively, the vehicle search platform maygenerate object characteristics data for the one or more in-vehicleobjects (e.g., in the manner described above) and/or may update the userprofile and/or the other profiles based on the object characteristicsdata.

In this way, the vehicle search platform determines usercharacteristics, object characteristics, and/or vehicle preferences thatmay be used to assist the user in making vehicle purchasing decisions.

As shown in FIG. 1D, and by reference number 135, the user may access asearch feature of the vehicle search application. For example, the usermay access an interface of the vehicle search application (e.g., awebpage of a website, an interface of a mobile application, and/or thelike), which may display the set of search parameters that are availableto the user. The user may interact with the search feature to selectparticular search parameters that are to be included as part of avehicle search request.

As shown by reference number 140, the vehicle search platform mayreceive the vehicle search request from the user device. For example,the user may interact with the interface of the search feature to submitthe vehicle search request, which may include an identifier of the user,an identifier of the user device, one or more search parameteridentifiers corresponding to one or more search parameters that havebeen selected, and/or the like. This may cause the vehicle searchrequest to be provided to the vehicle search platform (e.g., via an APIor another type of communication interface).

As shown by reference number 145, the vehicle search platform mayidentify a set of vehicles to recommend to the user. For example, thevehicle search platform may have access to a data structure thatassociates a set of vehicle identifiers for a collection of vehiclesthat are being offered for sale with corresponding vehicle data for thecollection of vehicles (e.g., vehicle description data for thecollection of vehicles, vehicle image data for the collection ofvehicles, and/or the like). In this case, the vehicle search platformmay identify a set of vehicles, from the collection of vehicles, byusing a set of search rules to filter the collection of vehicles (e.g.,which may be selected based on selected search parameters), by using adata model that has been trained using machine learning to identifyvehicles that are predicted to be compatible with the user, and/or thelike, as each described below.

In some implementations, the vehicle search platform may identify one ormore vehicles to recommend based on one or more search rules. The one ormore search rules may correspond to the one or more search parametersthat have been selected and may be used to filter the collection ofvehicles. The one or more search rules may, for example, include a firstsearch rule indicating that a vehicle is to be excluded from searchresults if a height of the user satisfies a threshold height (e.g., athreshold height may correspond to a particular type of vehicle and maybe based on a distance between a first point on a seat of the vehicleand a second point of a ceiling of the vehicle), a second search ruleindicating that a vehicle is to be excluded from the search results if awidth of the user satisfies a threshold width (e.g., a threshold widthmay correspond to a width of a seat found in a particular type ofvehicle), a third search rule indicating that a vehicle is to beexcluded from search results if the vehicle does not comply with avehicle preference of the user, a fourth search rule indicating that avehicle is to be excluded from search results based on feedback providedby the user, and/or the like. Vehicle data for the collection ofvehicles may be stored in association with the one or more search rulesand/or identifiers for the one or more search rules, which may allow thevehicle search platform to filter the collection of vehicles based onthe one or more search rules.

As an example, the vehicle search platform may receive a custom searchparameter indicating to filter vehicles based on a height of the user.In this example, the vehicle search platform may obtain data identifyingthe height of the user (e.g., from the user profile), and may comparethe height of the user and a set of threshold heights that correspond tothe collection of vehicles. As a specific example, vehicle data for avehicle may be stored in association with a threshold height of 6′ 4″,where the threshold height is configured based on an estimation thatusers that are 6′ 4″ or taller are unlikely to fit into the vehicle. If,for example, the height of the user satisfies (e.g., is greater than)the threshold height corresponding to the vehicle, the vehicle searchplatform may exclude the vehicle from the search results.

Additionally, or alternatively, the vehicle search platform may identifyone or more vehicles to recommend using the data model. For example, thevehicle search platform may have trained a data model (or received atrained data model) and may use the data model to generate scores thatindicate likelihoods of each vehicle, in the collection of vehicles,being compatible with the user. The vehicle search platform may, basedon the scores, identify one or more vehicles, of the collection ofvehicles, to recommend to the user. Descriptions of training the datamodel and using the data model are provided below.

In some implementations, the vehicle search platform may have determineda set of training features that may be used to train the data model. Forexample, the vehicle search platform may perform a set of featureidentification techniques to process historical data to identify a setof training features that may be used to train the data model. The setof feature identification techniques may include using a technique thatinvolves text mining and latent semantic analysis (LSA), a trendvariable analysis technique, an interest diversity analysis technique, atechnique using a neural network, a composite indicators analysistechnique, a clustering technique, a regression technique, and/or thelike. The historical data may include historical vehicle data (e.g.,historical vehicle description data, historical 3-D renderings ofvehicles, and/or the like), historical user data (e.g., historical userpurchasing history data, historical user rendering data, historicalvehicle preference data, and/or the like), and/or the like.

A training feature may be a value (e.g., a numerical value, a charactervalue, and/or the like) or a group of values (e.g., a term thatcomprises multiple character values, a combination of terms, acombination of a numerical value, a character value, and/or a term,and/or the like) that increases a likelihood of a user being compatiblewith a vehicle and/or that decreases a likelihood of a user beingcompatible with a vehicle. The set of training features may include oneor more user characteristics (e.g., a characteristic indicating a heightof the user, a characteristic indicating a width of a user, and/or thelike), one or more user preferences, one or more vehicle characteristicsof particular vehicles, one or more trends (e.g., a trend that indicatesthat users that share a particular type of demographic information aremore likely to be compatible with particular types of vehicles),combinations of one or more other training features described above,and/or the like.

In some implementations, the vehicle search platform may perform afeature identification technique to determine the set of trainingfeatures. For example, the vehicle search platform may perform a trendvariable analysis technique to identify training features associatedwith changes to data over a given time period. In this case, the vehiclesearch platform may aggregate values periodically and may compare theaggregated values to identify changes over a given time period. This mayallow the vehicle search platform to identify trends or patterns thatmay be used as training features. To provide a few examples, the vehiclesearch platform may determine that an individual is 30% more likely tobe compatible with a vehicle if a height of the user falls within athreshold range of heights, 80% more likely to be compatible with avehicle if vehicle characteristics of the vehicle satisfy all of thevehicle preferences of the user, and/or the like.

In some implementations, the vehicle search platform may train the datamodel based on the set of training features. For example, the vehiclesearch platform may process the set of features using one or moremachine learning techniques to generate a data model that is able togenerate scores that indicate likelihoods of particular input datavalues (e.g., particular features) increasing or decreasing likelihoodsof particular vehicles being compatible with particular users. The oneor more machine learning techniques may include a classification-basedtechnique, a regression-based technique, a clustering-based technique, atechnique using a neural network, and/or the like.

As an example, the vehicle search platform may train a neural networkbased on the set of training features. In this example, the vehiclesearch platform may train a neural network that has an input layer, oneor more intermediate layers (e.g., a fully connected layer, aconvolutional layer, a pooling layer, a recurrent layer, and/or thelike), and an output layer. Additionally, the set of features may behyperparameters that are used as part of a cost function of the neuralnetwork. In this case, the vehicle search platform may perform afeedforward technique to provide particular historical data values(e.g., that are part of a test dataset) as input to the neural network,and the neural network may output scores that indicate likelihoods ofeach vehicle, of the collection of vehicles, being compatible with aparticular user.

Additionally, the vehicle search platform may compare scores to knownvalues and may determine an error value based on the comparison betweenthe scores and the known values. The error value may be used to updatethe cost function (e.g., which may assign weights to particular featuresof the set of features), and the vehicle search platform may perform aback-propagation technique to iteratively train the neural network untila threshold level of accuracy has been reached.

To use the data model that has been trained, the vehicle search platformmay determine a set of features (e.g., a subset of the set of trainingfeatures) based on the user rendering data and/or the vehicle preferencedata provided by the user device. For example, the vehicle searchplatform may use one or more feature identification techniques (asdescribed above) to determine a set of features that are based on dataprovided by the user device. In this case, the vehicle search platformmay provide, as input to the data model, the set of features to causethe data model to output a set of scores that indicate likelihoods ofeach vehicle, of the collection of vehicles, being compatible with theuser. Furthermore, the vehicle search platform may identify the set ofvehicles based on the set of scores. For example, the vehicle searchplatform may be configured with a threshold confidence score, and mayidentify the set of vehicles, from the collection of vehicles, based onthe set of vehicles corresponding to scores that satisfy the thresholdconfidence score.

In some implementations, the vehicle search platform may identify theset of vehicles using the one or more search rules and the data model.For example, the vehicle search platform may use the one or more searchrules to filter the collection of vehicles to identify a subset of thecollection of vehicles. In this case, the vehicle search platform maydetermine the set of features and may provide the set of features asinput to the data model to cause the data model to output a set ofscores that indicate likelihoods of each vehicle, of the subset of thecollection of vehicles, being compatible with the user. This may allowthe vehicle search platform to identify one or more vehicles torecommend to the user based on the set of scores. By filtering thecollection of vehicles based on the one or more search rules beforeusing the data model, the vehicle search platform reduces a utilizationof resources (e.g., processing resources, network resources, and/or thelike) relative to using the data model to process vehicle data for theentire collection of vehicles.

In this way, the vehicle search platform identifies the set of vehiclesto recommend to the user.

As shown in FIG. 1E, and by reference number 150, the vehicle searchplatform may cause the set of vehicles to be displayed on an interfaceof the user device. For example, if the vehicle search application isnot hosted on the vehicle search platform (e.g., the application may behosted on another device that is external to the vehicle searchplatform), then the vehicle search platform may provide vehicleidentifiers for the set of vehicles to the other device. This may allowthe other device to update the interface of the vehicle searchapplication to display vehicle data for the set of vehicles. The vehicledata that is displayed may include a list of vehicle identifiers for theset of vehicles, features of vehicles, images of the vehicles (e.g., 2-Dimages and/or 3-D images), and/or the like.

In other cases, the vehicle search application may be hosted on thevehicle search platform. In these cases, the vehicle search platform mayuse the vehicle identifiers for the vehicle identifiers to identify thevehicle data that is to be displayed, and to update the interface of thevehicle search application to display the vehicle data.

As shown by reference number 155, the user may interact with theinterface of the vehicle search application to select a vehicle. In someimplementations, the user may interact with the interface of the vehiclesearch application to select multiple vehicles, to select particularvehicle characteristics of vehicles, and/or the like. In someimplementations, the vehicle search platform may automatically selectone or more vehicles and/or vehicle characteristics (e.g., without theuser needing to interact with the interface). For example, the vehiclesearch platform may automatically select one or more vehicles based onthe scores that indicate likelihoods of particular vehicles beingcompatible with the user (e.g., as output by the data model), confidencevalues corresponding to the scores, one or more configurable rules thatspecify when the vehicle search platform may automatically select avehicle and/or a vehicle characteristic, and/or the like.

In the example shown, the interface may display the set of vehicles thatare being recommended (shown as Truck 1, Truck 2, Sports Utility Vehicle(SUV) 1, SUV 2, Jeep 1, and Sports Car 1), and the user may interactwith the interface to select a vehicle (shown as Sports Car 1). When thevehicle is selected, and as shown by reference number 160, userinteractions data that includes a vehicle identifier for the selectedvehicle may be provided to the vehicle search platform.

In this way, the user selects a vehicle from the set of vehicles thathas been recommended by the vehicle search platform.

As shown in FIG. 1F, and by reference number 165, the vehicle searchplatform may cause an interface of the vehicle search application todisplay a placement of the 3-D rendering of the user into a 3-Drendering of the selected vehicle. For example, the vehicle searchplatform may use an identifier of the user to obtain the 3-D renderingof the user (e.g., which may be stored as part of, or in associationwith, the profile information for the user). Additionally, the vehiclesearch platform may use the vehicle identifier for the selected vehicleto identify the 3-D rendering of the vehicle. In this case, the vehiclesearch platform may cause the interface of the vehicle searchapplication to display a placement of the 3-D rendering of the user intothe 3-D rendering of the selected vehicle. Additionally, the vehiclesearch platform may be configured with instructions that define areference point at which to place the 3-D rendering of the user into 3-Drendering of the vehicle, as further described below.

In the example shown, the vehicle search platform may cause the 3-Drendering of the user to be placed in a seat of the vehicle. In thisexample, the vehicle search platform may be configured with a referencepoint of a seat of a 3D rendering of a vehicle, may identify thereference point within the 3-D rendering of the vehicle, and may placethe 3-D rendering of the user into the 3-D rendering of the vehicle,such that the 3-D rendering of the user is placed relative to thereference point of the seat. This may allow the 3-D rendering of theuser to depict the user sitting in the seat, which may illustrate to theuser a degree to which the user fits in the seat.

In some implementations, the user may interact with an AR scene toselect where to place the 3-D rendering of the user. For example, thevehicle search platform may launch an AR scene that may be displayed onthe interface of the vehicle search application, such that the interfacedisplays the 3-D rendering of the vehicle. In this case, the user mayinteract with the interface (e.g., by selecting and/or pointing to alocation on the 3-D rendering of the vehicle), such as by interactingwith a 3-D representation of a seat of the vehicle, a door of thevehicle, and/or the like. This may cause the vehicle search platform toplace the 3-D rendering of the user based on the selected location. Forexample, the vehicle search platform may place the 3-D rendering of theuser such that the 3-D rendering of the user is sitting within aselected seat of the vehicle, may place the 3-D rendering of the usersuch that the 3-D rendering of the user is opening a door of thevehicle, and/or the like.

In some implementations, the user may control a position of the 3-Drendering of the user within the AR scene. For example, if the 3-Drendering of the user has been placed in a seat of a vehicle, the usermay be able to move the hands of the 3-D rendering of the user to grabthe steering wheel, may be able to move the feet of the 3-D rendering ofthe user onto a brake pedal and/or a gas pedal of the vehicle, and/orthe like (e.g., which may allow the user to determine whether the userfits in the seat, whether the seat needs to be adjusted, and/or thelike).

In some implementations, the user may interact with one or moreconfigurable settings of the 3-D rendering of the vehicle. For example,the user may interact with the interface to adjust a seat placement of aseat of the vehicle. Additionally, the user may select and/or point to aseat adjustment control button and may select a particular seatplacement. In this case, the vehicle search platform may cause theinterface depicting the AR scene to display a sequence of 3-D renderingsof the vehicle, such that the seat of the vehicle updates on theinterface from an initial seat placement to the particular seatplacement selected by the user.

In some implementations, the vehicle search platform may place 3-Drenderings of multiple users into the 3-D rendering of the vehicle. Forexample, the user may have created a 3-D rendering of the user and oneor more additional 3-D renderings of one or more other family members.In this case, the vehicle search platform may cause the interface of thevehicle search application to display the 3-D rendering of the userand/or the one or more additional 3-D renderings of the one or moreother family members. In some cases, the user may select a 3-D renderingof a particular user to place into the 3-D rendering of the vehicle(e.g., by interacting with the interface), may select a location withinthe vehicle at which to place the 3-D rendering of the particular user,and/or the like.

In some implementations, the vehicle search platform may place 3-Drenderings of in-vehicle objects into the 3-D rendering of the vehicle.For example, the user may have created a 3-D rendering of a car seat fora child, of a glass or cup that may be placed into a cup holder, of asuitcase that may be placed into a trunk, and/or the like. In this case,the vehicle search platform may cause the interface to display the 3-Drenderings of the in-vehicle objects within the 3-D rendering of thevehicle. In some cases, the user may select a 3-D rendering of aparticular in-vehicle object to place into the 3-D rendering of thevehicle, may select a location within the vehicle at which to place the3-D rendering of the particular in-vehicle object, and/or the like.

In some implementations, the vehicle search platform may place the 3-Drendering of the user and/or 3-D renderings of in-vehicle objects into3-D renderings of multiple vehicles. For example, if the user and/or thevehicle search platform selected multiple vehicles, the vehicle searchplatform may place the 3-D rendering of the user and/or the 3-Drenderings of the in-vehicle objects into 3-D renderings of multiplevehicles. This may cause the interface to concurrently display imagesdepicting the user sitting within each of the multiple vehicles, displayimages depicting the in-vehicle objects within each of the multiplevehicles, and/or the like. This may allow the user to view how the userfits in multiple vehicles at the same time, thereby allowing the usermake a comparison regarding how the user fits in each respective vehiclerelative to the other vehicles.

In this way, the vehicle search platform uses AR to identify optimalvehicles to recommend to the user and, when the user selects a vehicle,provides the user with one or more illustrations that indicate a degreeto which the user, one or more other users, and/or one or morein-vehicle objects may be compatible with the vehicle.

As indicated above, FIGS. 1A-1F are provided merely as one or moreexamples. Other examples may differ from what is described with regardto FIGS. 1A-1F. For example, there may be additional devices and/ornetworks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIGS. 1A-1F. Furthermore, two or more devices shown in FIGS.1A-1F may be implemented within a single device, or a single deviceshown in FIGS. 1A-1F may be implemented as multiple and/or distributeddevices. Additionally, or alternatively, a set of devices (e.g., one ormore devices) included in the one or more example implementations 100may perform one or more functions described as being performed byanother set of devices included in the one or more exampleimplementations 100.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2 ,environment 200 may include a user device 210, a data storage device220, a vehicle search platform 230 supported within a cloud computingenvironment 240, and a network 250. Devices of environment 200 mayinterconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

User device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith a vehicle search application. For example, user device 210 mayinclude a communication and/or computing device, such as a mobile phone(e.g., a smart phone, a radiotelephone, and/or the like), a laptopcomputer, a tablet computer, a handheld computer, a gaming device, awearable communication device (e.g., a smart wristwatch, a pair of smarteyeglasses, smart clothing, and/or the like), or a similar type ofdevice. In some implementations, user device 210 may include a display,an image capture component (e.g., a sensor, a camera, a camcorder,and/or the like), a location tracking component (e.g., a globalpositioning system (GPS), an accelerometer, and/or the like), and/or thelike. In some implementations, user device 210 may host, support, and/orhave access to a vehicle search application. In some implementations,user device 210 may host an augmented reality (AR) scene. In someimplementations, user device 210 may host, support, and/or have accessto a model generation tool. In some implementations, user device 210 mayperform one or more functions described herein as being performed byvehicle search platform 230. For example, user device 210 may processuser rendering data for a 3-D rendering of a user to determine a set ofuser characteristics of the user.

Data storage device 220 includes one or more devices capable ofreceiving, storing, generating, determining, and/or providing dataassociated with a vehicle search application. For example, data storagedevice 220 may include a server device or a group of server devices. Insome implementations, one or more data storage devices 220 may storevehicle rendering data that depicts three-dimensional (3-D) renderingsof vehicles, vehicle description data that describes the vehicles, userrendering data that depicts 3-D renderings of users, vehicle preferencesdata that describes vehicle preferences of users, and/or the like.

Vehicle search platform 230 includes one or more devices capable ofreceiving, storing, generating, determining, and/or providinginformation associated with a vehicle search application. For example,vehicle search platform 230 may include a server device (e.g., a hostserver, a web server, an application server, etc.), a data centerdevice, or a similar device. In some implementations, vehicle searchplatform 230 may host and/or support a vehicle search application,according to one or more implementations described herein.

In some implementations, as shown, vehicle search platform 230 may behosted in cloud computing environment 240. While implementationsdescribed herein describe vehicle search platform 230 as being hosted incloud computing environment 240, in some implementations, vehicle searchplatform 230 may be non-cloud-based (i.e., may be implemented outside ofa cloud computing environment) or may be partially cloud-based.

Cloud computing environment 240 includes an environment that hostsvehicle search platform 230. Cloud computing environment 240 may providecomputation, software, data access, storage, etc. services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that hosts vehicle search platform 230. Asshown, cloud computing environment 240 may include a group of computingresources 235 (referred to collectively as “computing resources 235” andindividually as “computing resource 235”).

Computing resource 235 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource235 may host vehicle search platform 230. The cloud resources mayinclude compute instances executing in computing resource 235, storagedevices provided in computing resource 235, data transfer devicesprovided by computing resource 235, and/or the like. In someimplementations, computing resource 235 may communicate with othercomputing resources 235 via wired connections, wireless connections, ora combination of wired and wireless connections.

As further shown in FIG. 2 , computing resource 235 may include a groupof cloud resources, such as one or more applications (“APPs”) 235-1, oneor more virtual machines (“VMs”) 235-2, virtualized storage (“VSs”)235-3, one or more hypervisors (“HYPs”) 235-4, and/or the like.

Application 235-1 may include one or more software applications that maybe provided to or accessed by user device 210 and/or data storage device220. Application 235-1 may eliminate a need to install and execute thesoftware applications on these devices. For example, application 235-1may include software associated with vehicle search platform 230 and/orany other software capable of being provided via cloud computingenvironment 240. In some implementations, one application 235-1 maysend/receive information to/from one or more other applications 235-1,via virtual machine 235-2. In some implementations, application 235-1may include the vehicle search application. Additionally, oralternatively, application 235-1 may include the model generation tool.

Virtual machine 235-2 may include a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 235-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 235-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 235-2 may execute on behalf of anotherdevice (e.g., user device 210, data storage device 220, and/or thelike), and may manage infrastructure of cloud computing environment 240,such as data management, synchronization, or long-duration datatransfers.

Virtualized storage 235-3 may include one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 235. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 235-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 235.Hypervisor 235-4 may present a virtual operating platform to the guestoperating systems and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 250 includes one or more wired and/or wireless networks. Forexample, network 250 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, etc.), a public land mobile network (PLMN), a localarea network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a telephone network (e.g., the Public Switched TelephoneNetwork (PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, or thelike, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 2 . Furthermore, two or more devices shown inFIG. 2 may be implemented within a single device, or a single deviceshown in FIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210, data storage device 220, and/orvehicle search platform 230. In some implementations, user device 210,data storage device 220, and/or vehicle search platform 230 may includeone or more devices 300 and/or one or more components of device 300. Asshown in FIG. 3 , device 300 may include a bus 310, a processor 320, amemory 330, a storage component 340, an input component 350, an outputcomponent 360, and a communication interface 370.

Bus 310 includes a component that permits communication among multiplecomponents of device 300. Processor 320 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 320includes a central processing unit (CPU), a graphics processing unit(GPU), an accelerated processing unit (APU), a microprocessor, amicrocontroller, a digital signal processor (DSP), a field-programmablegate array (FPGA), an application-specific integrated circuit (ASIC),and/or another type of processing component. In some implementations,processor 320 includes one or more processors capable of beingprogrammed to perform a function. Memory 330 includes a random accessmemory (RAM), a read only memory (ROM), and/or another type of dynamicor static storage device (e.g., a flash memory, a magnetic memory,and/or an optical memory) that stores information and/or instructionsfor use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 360 includes a component thatprovides output information from device 300 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 300 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 370 may permit device300 to receive information from another device and/or provideinformation to another device. For example, communication interface 370may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3 . Additionally, or alternatively,a set of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for using augmentedreality (AR) to assist a user in searching for a vehicle. In someimplementations, one or more process blocks of FIG. 4 may be performedby a vehicle search platform (e.g., vehicle search platform 230). Insome implementations, one or more process blocks of FIG. 4 may beperformed by another device or a group of devices separate from orincluding the vehicle search platform, such as a user device (e.g., userdevice 210), a data storage device (e.g., data storage device 220),and/or the like.

As shown in FIG. 4 , process 400 may include receiving user renderingdata for a three-dimensional (3-D) rendering of a user, wherein the 3-Drendering is a proportional representation of the user, and wherein theuser rendering data is to be made available to an application thatassists with vehicle purchases (block 410). For example, the vehiclesearch platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationinterface 370, and/or the like) may receive user rendering data for athree-dimensional (3-D) rendering of a user, as described above. In someimplementations, the 3-D rendering may be a proportional representationof the user. In some implementations, the user rendering data may bemade available to an application that assists with vehicle purchases.

As further shown in FIG. 4 , process 400 may include determining, byprocessing the user rendering data, a set of user characteristics of theuser (block 420). For example, the vehicle search platform (e.g., usingcomputing resource 235, processor 320, memory 330, storage component340, and/or the like) may determine, by processing the user renderingdata, a set of user characteristics of the user, as described above.

As further shown in FIG. 4 , process 400 may include receiving anindication that a user device has submitted a vehicle search request(block 430). For example, the vehicle search platform (e.g., usingcomputing resource 235, processor 320, memory 330, storage component340, input component 350, communication interface 370, and/or the like)may receive an indication that the user device (e.g., user device 210)has submitted a vehicle search request, as described above.

As further shown in FIG. 4 , process 400 may include identifying a setof vehicles to recommend to the user based on an analysis of: the set ofuser characteristics, and a set of vehicle characteristics for acollection of vehicles being offered via the application (block 440).For example, the vehicle search platform (e.g., using computing resource235, processor 320, memory 330, storage component 340, and/or the like)may identify a set of vehicles to recommend to the user based on ananalysis of: the set of user characteristics, and a set of vehiclecharacteristics for a collection of vehicles being offered via theapplication, as described above.

As further shown in FIG. 4 , process 400 may include causing vehicledescription data for the set of vehicles to be displayed via aninterface of the application (block 450). For example, the vehiclesearch platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370, and/or the like) may cause vehicledescription data for the set of vehicles to be displayed via aninterface of the application, as described above.

As further shown in FIG. 4 , process 400 may include receiving userinteraction data that indicates a user selection of a particular vehicleof the set of vehicles (block 460). For example, the vehicle searchplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, input component 350, communication interface 370,and/or the like) may receive user interaction data that indicates a userselection of a particular vehicle of the set of vehicles, as describedabove.

As further shown in FIG. 4 , process 400 may include causing, based onreceiving the user interaction data, the interface of the application todisplay a placement of the 3-D rendering of the user into a 3-Drendering of the particular vehicle, wherein the placement creates avisual that illustrates a degree to which the user is compatible withthe particular vehicle (block 470). For example, the vehicle searchplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may cause, based onreceiving the user interaction data, the interface of the application todisplay a placement of the 3-D rendering of the user into a 3-Drendering of the particular vehicle, as described above. In someimplementations, the placement may create a visual that illustrates adegree to which the user is compatible with the particular vehicle.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the visual illustrates the 3-D rendering ofthe user sitting on a seat within the 3-D rendering of the particularvehicle.

In a second implementation, alone or in combination with the firstimplementation, the vehicle search platform may, before receiving theindication that the user device has submitted the vehicle searchrequest, integrate the set of user characteristics into a search featureof the application to permit the search feature to offer searchparameters that are customized to the user.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, the vehicle search platform may,when identifying the set of vehicles to recommend, analyze the set ofuser characteristics and the set of vehicle characteristics using a datamodel that has been trained using one or more machine learningtechniques, and may identify the set of vehicles based on the set ofscores.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, the vehicle search platformmay, when causing the vehicle description data for the set of vehiclesto be displayed via the interface, cause the interface to display, aspart of the vehicle description data, the set of scores that indicatethe likelihoods of one or more of the collection of vehicles beingcompatible with the user.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, the vehicle search platformmay receive, from the user device and before identifying the set ofvehicles to recommend to the user, vehicle preference data thatidentifies a set of one or more vehicle preferences of the user. In someimplementations, when identifying the set of vehicles to recommend, thevehicle search platform may identify the set of vehicles to recommend tothe user based on the vehicle preference data.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, the vehicle search platform mayreceive, before identifying the set of vehicles, object rendering datafor a 3-D rendering of a car seat for a child. In some implementations,when identifying the set of vehicles to recommend the vehicle searchplatform may identify the set of vehicles to recommend based on theobject rendering data.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4 . Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for using augmentedreality (AR) to assist a user in searching for a vehicle. In someimplementations, one or more process blocks of FIG. 5 may be performedby a vehicle search platform (e.g., vehicle search platform 230). Insome implementations, one or more process blocks of FIG. 5 may beperformed by another device or a group of devices separate from orincluding the vehicle search platform, such as a user device (e.g., userdevice 210), a data storage device (e.g., data storage device 220),and/or the like.

As shown in FIG. 5 , process 500 may include receiving image data thatdepicts at least a portion of a user of an application that assists withvehicle purchases (block 510). For example, the vehicle search platform(e.g., using computing resource 235, processor 320, memory 330, storagecomponent 340, input component 350, communication interface 370, and/orthe like) may receive image data that depicts at least a portion of auser of an application that assists with vehicle purchases, as describedabove.

As further shown in FIG. 5 , process 500 may include generating userrendering data for a three-dimensional (3-D) rendering of at least theportion of the user depicted in the image data, wherein the 3-Drendering is based on measurements of the user (block 520). For example,the vehicle search platform (e.g., using computing resource 235,processor 320, memory 330, storage component 340, and/or the like) maygenerate user rendering data for a three-dimensional (3-D) rendering ofat least the portion of the user depicted in the image data, asdescribed above. In some implementations, the 3-D rendering is based onmeasurements of the user.

As further shown in FIG. 5 , process 500 may include determining, byprocessing the user rendering data, a set of user characteristics of theuser (block 530). For example, the vehicle search platform (e.g., usingcomputing resource 235, processor 320, memory 330, storage component340, and/or the like) may determine, by processing the user renderingdata, a set of user characteristics of the user, as described above.

As further shown in FIG. 5 , process 500 may include receiving anindication that a user device associated with the user is accessing asearch feature of the application (block 540). For example, the vehiclesearch platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationinterface 370, and/or the like) may receive an indication that a userdevice associated with the user is accessing a search feature of theapplication, as described above.

As further shown in FIG. 5 , process 500 may include identifying a setof vehicles to recommend to the user based on a machine-learning-drivenanalysis of at least one of: one or more search parameters that the userdevice provided to the search feature of the application the set of usercharacteristics, or and a set of vehicle characteristics for acollection of vehicles being offered via the application (block 550).For example, the vehicle search platform (e.g., using computing resource235, processor 320, memory 330, storage component 340, and/or the like)may identify a set of vehicles to recommend to the user based on amachine-learning-driven analysis of at least one of: one or more searchparameters that the user device provided to the search feature of theapplication, the set of user characteristics, or and a set of vehiclecharacteristics for a collection of vehicles being offered via theapplication, as described above.

As further shown in FIG. 5 , process 500 may include causing vehicledescription data for the set of vehicles to be displayed via aninterface of the application (block 560). For example, the vehiclesearch platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370, and/or the like) may cause vehicledescription data for the set of vehicles to be displayed via aninterface of the application, as described above.

As further shown in FIG. 5 , process 500 may include receiving userinteraction data that indicates a user selection of a particular vehicleof the set of vehicles (block 570). For example, the vehicle searchplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, input component 350, communication interface 370,and/or the like) may receive user interaction data that indicates a userselection of a particular vehicle of the set of vehicles, as describedabove.

As further shown in FIG. 5 , process 500 may include causing, based onreceiving the user interaction data, the interface of the application todisplay a placement of the 3-D rendering of the user into a 3-Drendering of the particular vehicle, wherein the placement creates avisual that illustrates a degree to which the user is compatible withthe particular vehicle (block 580). For example, the vehicle searchplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370, and/or the like) may cause, based onreceiving the user interaction data, the interface of the application todisplay a placement of the 3-D rendering of the user into a 3-Drendering of the particular vehicle, as described above. In someimplementations, the placement may create a visual that illustrates adegree to which the user is compatible with the particular vehicle.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the visual may illustrate the 3-D renderingof the user sitting on a seat within the 3-D rendering of the particularvehicle. In a second implementation, alone or in combination with thefirst implementation, the set of user characteristics may include atleast one of: a height of the user, a width of one or more body parts ofthe user, or a length of the one or more body parts of the user. In someimplementations, the set of vehicle characteristics for the particularvehicle include at least one of: a first seat placement, that isassociated with a minimum seat extension distance, for one or more seatsincluded within the particular vehicle, a second seat placement, that isassociated with a maximum seat extension distance, for the one or moreseats included in the particular vehicle, a distance between a firstpoint of the one or more seats and a second point of a ceiling of theparticular vehicle.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, the vehicle search platform mayintegrate the set of user characteristics into the search feature topermit the search feature to offer a set of search parameters that arecustomized to the user. In a fourth implementation, alone or incombination with one or more of the first through third implementations,the vehicle search platform, when identifying the set of vehicles torecommend, may analyze the set of user characteristics and the set ofvehicle characteristics using a data model that has been trained to usemachine learning to identify compatible vehicles based on vehiclespurchased by other users that have similar characteristics to the user.The data model may output a set of scores that indicate likelihoods ofthe collection of vehicles being compatible with the user. Additionally,the vehicle search platform may identify the set of vehicles based onthe set of scores.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, the vehicle search platformmay identify, from the user device and before identifying the set ofvehicles to recommend to the user, vehicle preference data thatidentifies a set of vehicle preferences of the user. In someimplementations, when identifying the set of vehicles to recommend, thevehicle search platform may identify the set of vehicles to recommend tothe user based on the machine-learning-driven analysis. In someimplementations, the machine-learning-driven analysis may includeanalyzing the vehicle preference data.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, the vehicle search platform mayreceive, before identifying the set of vehicles to recommend, objectrendering data for a 3-D rendering of a car seat for a child. In someimplementations, when identifying the set of vehicles to recommend, thevehicle search platform identify the set of vehicles to recommend basedon the object rendering data.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for using augmentedreality (AR) to assist a user in searching for a vehicle. In someimplementations, one or more process blocks of FIG. 6 may be performedby a vehicle search platform (e.g., vehicle search platform 230). Insome implementations, one or more process blocks of FIG. 6 may beperformed by another device or a group of devices separate from orincluding the vehicle search platform, such as a user device (e.g., userdevice 210), a data storage device (e.g., data storage device 220),and/or the like.

As shown in FIG. 6 , process 600 may include receiving user renderingdata for a three-dimensional (3-D) rendering of a user, wherein the 3-Drendering is based on measurements of the user and is a proportionalrepresentation of the user, and wherein the user rendering data is to bemade available to an application that assists with vehicle purchases(block 610). For example, the vehicle search platform (e.g., usingcomputing resource 235, processor 320, memory 330, storage component340, input component 350, communication interface 370, and/or the like)may receive user rendering data for a three-dimensional (3-D) renderingof a user, as described above. In some implementations, the 3-Drendering may be based on measurements of the user and may be aproportional representation of the user. In some implementations, theuser rendering data may be made available to an application that assistswith vehicle purchases.

As further shown in FIG. 6 , process 600 may include determining, byprocessing the user rendering data, a set of user characteristics of theuser (block 620). For example, the vehicle search platform (e.g., usingcomputing resource 235, processor 320, memory 330, storage component340, and/or the like) may determine, by processing the user renderingdata, a set of user characteristics of the user, as described above.

As further shown in FIG. 6 , process 600 may include integrating the setof user characteristics into a search feature of the application topermit the search feature to offer a set of search parameters that arecustomized to the user (block 630). For example, the vehicle searchplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, and/or the like) may integrate the set of usercharacteristics into a search feature of the application to permit thesearch feature to offer a set of search parameters that are customizedto the user, as described above.

As further shown in FIG. 6 , process 600 may include receiving anindication that a user device associated with the user is accessing thesearch feature of the application (block 640). For example, the vehiclesearch platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationinterface 370, and/or the like) may receive an indication that a userdevice associated with the user is accessing the search feature of theapplication, as described above.

As further shown in FIG. 6 , process 600 may include identifying a setof vehicles to recommend to the user, based on an analysis of: one ormore search parameters that the user device provided to the searchfeature of the application, the set of user characteristics, and a setof vehicle characteristics for a collection of vehicles being offeredvia the application (block 650). For example, the vehicle searchplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, and/or the like) may identify a set of vehiclesto recommend to the user, based on an analysis of: one or more searchparameters that the user device provided to the search feature of theapplication, the set of user characteristics, and a set of vehiclecharacteristics for a collection of vehicles being offered via theapplication, as described above.

As further shown in FIG. 6 , process 600 may include providing vehicledescription data for the set of vehicles for display via an interface ofthe application (block 660). For example, the vehicle search platform(e.g., using computing resource 235, processor 320, memory 330, storagecomponent 340, output component 360, communication interface 370, and/orthe like) may provide vehicle description data for the set of vehiclesfor display via an interface of the application, as described above.

As further shown in FIG. 6 , process 600 may include causing theinterface of the application to display a placement of the 3-D renderingof the user into a 3-D rendering of at least one vehicle of the set ofvehicles, wherein the placement creates a visual that illustrates adegree to which the user is compatible with the at least one vehicle(block 670). For example, the vehicle search platform (e.g., usingcomputing resource 235, processor 320, memory 330, storage component340, input component 350, output component 360, communication interface370, and/or the like) may cause the interface of the application todisplay a placement of the 3-D rendering of the user into a 3-Drendering of at least one vehicle of the set of vehicles, as describedabove. In some implementations, the placement may create a visual thatillustrates a degree to which the user is compatible with the at leastone vehicle.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the vehicle search platform may receive,before receiving the user rendering data, a particular indication thatthe user device created an account for the application. In someimplementations, the vehicle search platform may provide, to the userdevice, a set of instructions that describe how to capture image data orvideo data that depicts the user in a manner that will be sufficient forgenerating the 3-D rendering of the user. In some implementations, theset of instructions may allow the user device to generate and providethe user rendering data to the vehicle search platform.

In a second implementation, alone or in combination with the firstimplementation, the visual illustrates the 3-D rendering of the usersitting on a seat within the 3-D rendering of the particular vehicle. Ina third implementation, alone or in combination with one or more of thefirst and second implementations, the vehicle search platform, whenidentifying the set of vehicles to recommend, may analyze the set ofuser characteristics and the set of vehicle characteristics using a datamodel that has been trained using one or more machine learningtechniques. The data model may output a set of scores that indicatelikelihoods of the collection of vehicles being compatible with the useridentify the set of vehicles based on the set of scores. Additionally,the vehicle search platform may identify the set of vehicles based onthe set of scores.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, the vehicle search platform mayreceive, from the user device and before identifying the set of vehiclesto recommend to the user, vehicle preference data that identifies a setof vehicle preferences of the user. In some implementations, whendetermining the set of vehicles to recommend, the vehicle searchplatform may identify the set of vehicles to recommend to the user basedon the vehicle preference data.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, the vehicle search platformmay receive, before identifying the set of vehicles, additional userrendering data for one or more 3-D renderings of one or more otherusers. In some implementations, when identifying the set of vehicles torecommend, the vehicle search platform may identify the set of vehiclesto recommend based on the additional user rendering data.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, or the like.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, and/or thelike. A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: receiving, by a device,data for a first rendering of a proportional representation of a user;determining, by the device and based on the data, a set of firstcharacteristics associated with the user; processing, by the device andusing a machine learning model, the set of first characteristics and aset of second characteristics associated with one or more objects,wherein the processing identifies a set of objects that are compatiblewith the user, and wherein the one or more objects include the set ofobjects; and causing, by the device, placement of the first rendering ina second rendering of a particular object of the set of objects.
 2. Themethod of claim 1, wherein the first rendering and the second renderingare associated with a three-dimensional (3-D) rendering.
 3. The methodof claim 1, wherein identifying the set of objects is further based ondetermining whether a particular characteristic of the set of secondcharacteristics satisfies a threshold.
 4. The method of claim 1, furthercomprising: integrating the set of first characteristics into a searchfeature; and providing, based on the set of first characteristics beingintegrated into the search feature, a set of search parameters that arecustomized.
 5. The method of claim 1, wherein the identifying the set ofobjects is further based on a score that indicates a likelihood of theparticular object being compatible with the user, and wherein the scoreis based on a data model trained with historical data.
 6. The method ofclaim 1, wherein the set of objects is associated with a set ofvehicles, and wherein the set of second characteristics is associatedwith a set of seats of the set of vehicles.
 7. The method of claim 1,further comprising: providing information that defines a reference pointat which to place the first rendering into the second rendering.
 8. Adevice, comprising: one or more memories; and one or more processors,coupled to the one or more memories, configured to: receive data for afirst rendering of a proportional representation of a user; determine,based on the data, a set of first characteristics associated with theuser; process, using a machine learning model, the set of firstcharacteristics and a set of second characteristics associated with oneor more objects, wherein the processing identifies a set of objects thatare compatible with the user, and wherein the one or more objectsinclude the set of objects; and cause placement of the first renderingin a second rendering of a particular object of the set of objects. 9.The device of claim 8, wherein the first rendering and the secondrendering are associated with a three-dimensional (3-D) rendering. 10.The device of claim 8, wherein identifying the set of objects is furtherbased on determining whether a particular characteristic of the set ofsecond characteristics satisfies a threshold.
 11. The device of claim 8,wherein the one or more processors are further configured to: integratethe set of first characteristics into a search feature; and provide,based on the set of first characteristics being integrated into thesearch feature, a set of search parameters that are customized.
 12. Thedevice of claim 8, wherein the identifying the set of objects is furtherbased on a score that indicates a likelihood of the particular objectbeing compatible with the user, and wherein the score is based on a datamodel trained with historical data.
 13. The device of claim 8, whereinthe set of objects is associated with a set of vehicles, and wherein theset of second characteristics is associated with a set of seats of theset of vehicles.
 14. The device of claim 8, wherein the one or moreprocessors are further configured to: provide information that defines areference point at which to place the first rendering into the secondrendering.
 15. A non-transitory computer-readable medium storing a setof instructions, the set of instructions comprising: one or moreinstructions that, when executed by one or more processors of a device,cause the device to: receive data for a first rendering of aproportional representation of a user; determine, based on the data, aset of first characteristics associated with the user; process, using amachine learning model, the set of first characteristics and a set ofsecond characteristics associated with one or more objects, wherein theprocessing identifies a set of objects that are compatible with theuser, and wherein the one or more objects include the set of objects;and cause placement of the first rendering in a second rendering of aparticular object of the set of objects.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the first rendering andthe second rendering are associated with a three-dimensional (3-D)rendering.
 17. The non-transitory computer-readable medium of claim 15,wherein identifying the set of objects is further based on determiningwhether a particular characteristic of the set of second characteristicssatisfies a threshold.
 18. The non-transitory computer-readable mediumof claim 15, wherein the one or more instructions further cause thedevice to: integrate the set of first characteristics into a searchfeature; and provide, based on the set of first characteristics beingintegrated into the search feature, a set of search parameters that arecustomized.
 19. The non-transitory computer-readable medium of claim 15,wherein the identifying the set of objects is further based on a scorethat indicates a likelihood of the particular object being compatiblewith the user, and wherein the score is based on a data model trainedwith historical data.
 20. The non-transitory computer-readable medium ofclaim 15, wherein the set of objects is associated with a set ofvehicles, and wherein the set of second characteristics is associatedwith a set of seats of the set of vehicles.